Optimal Characterization of Unknown Multispecies Reactive Contamination Sources in an Aquifer
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Volume 26, Issue 11
Abstract
Groundwater contamination source characterization is the prerequisite for the rehabilitation and remediation of contaminated aquifers. This study demonstrates the computational feasibility and application of an efficient linked simulation-optimization based methodology for optimal characterization of multicomponent reactive contamination sources in a complex contaminated aquifer. The computational encumbrance of the complex reactive transport simulation is ameliorated using sufficiently accurate multioutput support vector regression (MSVR)–trained surrogate models as embedded constraints within the simulation-optimization framework. Three different metaheuristic optimization algorithms, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and generalized simulated annealing (GSA), were used, and their comparative performances were evaluated for a contaminated aquifer resembling a filed-scale contaminated mine site located in Northern Territory, Australia. The performance evaluation result based on an actual contaminated aquifer site along with synthetic concentration measurements indicated that the performance of the GSA-based source identification model is superior compared to PSO and GA. The reliability and robustness of the GSA model were further investigated for erroneous concentration measurement scenarios. The preliminary findings demonstrated the computational efficiency and accuracy of the proposed MSVR surrogate model–assisted optimization for simultaneous identification of multicomponent reactive sources at complex geochemically contaminated field-scale aquifers.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly appreciate James Cook University and the Australian Government Research Training Program Scholarship for providing financial support to the first author.
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Journal of Hydrologic Engineering
Volume 26 • Issue 11 • November 2021
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© 2021 American Society of Civil Engineers.
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Received: Jan 22, 2021
Accepted: Jul 19, 2021
Published online: Sep 10, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 10, 2022
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